Global Particle Physics Excellence Awards

  Physics-Informed Deep Learning for Virtual Rail Train Trajectory Following Control Physics-Informed Deep Learning for Virtual Rail Train Trajectory Following Control In recent years, advancements in artificial intelligence have reshaped the way we approach complex control systems, especially in the transportation sector. One of the most promising applications is the use of Physics-Informed Deep Learning (PIDL) for Virtual Rail Train Trajectory Following Control . This approach combines the predictive power of machine learning with the reliability of physical laws, providing a robust solution for real-time trajectory control in autonomous rail systems. Virtual Rail Technology Virtual rail technology refers to a system where trains follow a digitally defined path without requiring physical rails for guidance. Instead, trains use sensors, GPS, and control algorithms to stay within a virtual corridor, much like how autonomous cars follow lanes using camera and sensor data. This inn...

                     Women Researcher Award Women researchers have played a pivotal role in shaping the world of science, technology, medicine, and innovation, yet their contributions have often been overlooked or underappreciated. Recognizing the need for gender equity and the importance of diversity in research, numerous awards have been established worldwide to honor and celebrate the achievements of women researchers. These awards serve not only to acknowledge excellence but also to inspire future generations of women to pursue careers in research and academia. The Women Researcher Award is a significant milestone in this journey, highlighting groundbreaking discoveries, leadership, and contributions across various disciplines.  Women researchers have made remarkable contributions in fields such as medicine, engineering, environmental science, physics, chemistry, and artificial intelligence, often overcoming gender-based b...

 A physics-informed 3D surrogate model for elastic fields in polycrystals Polycrystalline materials, which consist of numerous crystallites or grains, exhibit complex mechanical responses due to the interaction of their microstructural features. Accurately modeling the elastic fields in polycrystals is crucial for predicting material behavior under stress.  Traditional methods, such as finite element analysis (FEA), are computationally expensive, particularly when dealing with three-dimensional (3D) microstructures. To address this challenge, physics-informed 3D surrogate models offer a promising alternative by integrating physical principles directly into machine learning frameworks. Physics-informed neural networks (PINNs) bridge the gap between data-driven approaches and traditional physics-based models. These models encode governing equations, such as the Navier-Cauchy equations for elasticity, as part of the loss function. By enforcing physical laws during training, PINNs...

  Precisely constructing asymmetric triple atoms for highly efficient electrocatalysis Triple-atom catalysts (TACs) are promising for surpassing the activity of normal single-atom and dual-atom catalysts. However, the rational design and construction of TACs remain challenging. Herein, we developed asymmetric Pt-Ru-Co triple atoms (TAs) by using selective atomic layer deposition technology. Compared with the corresponding single-atom and dual-atom counterparts, they demonstrate superior electrocatalytic performance in both the hydrogen evolution reaction (HER) and hydrogen oxidation reaction (HOR).  Operando  X-ray absorption spectroscopy (XAS) revealed that the heterogeneous atoms within Pt-Ru-Co TAs have strong interactions and serve as active centers, synergistically accelerating reaction kinetics.  Additionally, theoretical calculations indicate that introducing Co atoms effectively optimizes the  d  orbital electronic structure of Pt and Ru, endowing e...

  Outstanding Academic Achievement Award Academic achievement is a cornerstone of personal and societal development, marking a key aspect of an individual’s journey toward success and fulfillment. It is often viewed as a direct reflection of intellectual ability, hard work, and perseverance. Achieving excellence in academics, whether in elementary education or higher studies, sets the foundation for future career prospects and personal growth. The process of acquiring knowledge, developing critical thinking, and honing problem-solving skills is integral to shaping well-rounded individuals who contribute meaningfully to society. In an ever-evolving global landscape, academic success is increasingly important as it provides the necessary tools to adapt to technological advancements, global challenges, and dynamic job markets. Students who excel academically are often more adept at navigating complex situations, making informed decisions, and demonstrating leadership qualities in vari...

  Evaluation of thermal scattering law and cross sections for liquid hydrogen fluoride ☆ Liquid anhydrous hydrogen fluoride (HF) is a material commonly used in fuel manufacturing and processing, and as a result it is of particular interest for criticality safety applications. In order to capture the thermal scattering impacts from this hydrogenous material, accurate thermal scattering law (TSL, i.e. S(α,β)) libraries were developed.  Using classical molecular dynamics (MD) simulation of the liquid HF system, a parametrized three-site model was developed in the GROMACS MD code to accurately represent the hydrogen bond and capture the liquid’s interatomic structure. This computational model (referenced as the NCSU HF model) was constructed with a massless charge to capture the hydrogen bonds between molecules.  The accuracy of the NCSU HF model was verified by comparing its predictions of various HF properties with experimental data for the hydrogen and fluorine bond length...